Double-deck elevator systems using genetic network programming with reinforcement learning

Jin Zhou*, Lu Yu, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu, Sandor Markon

*この研究の対応する著者

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

In order to increase the transportation capability of elevator group systems in high-rise buildings without adding elevator installation space, double-deck elevator system (DDES) is developed as one of the next generation elevator group systems. Artificial intelligence (AI) technologies have been employed to And some efficient solutions in the elevator group control systems during the late 20th century. Genetic Network Programming (GNP), a new evolutionary computation method, is reported to be employed as the elevator group system controller in some studies of recent years. Moreover, reinforcement learning (RL) is also verified to be useful for more improvements of elevator group performances when it is combined with GNP. In this paper, we proposed a new approach of DDES using GNP with RL, and did some experiments on a simulated elevator group system of a typical office building to check its efficiency. Simulation results show that the DDES using GNP with RL performs better than the one without RL in regular and down-peak time, while both of them outperforms a conventional approach and a heuristic approach in all three traffic patterns.

本文言語English
ホスト出版物のタイトル2007 IEEE Congress on Evolutionary Computation, CEC 2007
ページ2025-2031
ページ数7
DOI
出版ステータスPublished - 2007 12 1
イベント2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
継続期間: 2007 9 252007 9 28

出版物シリーズ

名前2007 IEEE Congress on Evolutionary Computation, CEC 2007

Conference

Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007
国/地域Singapore
Period07/9/2507/9/28

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア
  • 理論的コンピュータサイエンス

フィンガープリント

「Double-deck elevator systems using genetic network programming with reinforcement learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル